Libraries
library(tidyverse)
library(hrbrthemes)
library(kableExtra)
library(ggplot2)
library(plotly)
setwd("D:\\akb")
coba <- read.csv("infant.csv", sep = ";")
attach(coba)
head(coba)
## Provinsi IMR
## 1 Aceh 27.18
## 2 Sumatera Utara 31.54
## 3 Sumatera Barat 30.40
## 4 Riau 22.40
## 5 Jambi 23.77
## 6 Sumatera Selatan 28.48
p <- coba %>%
filter(!is.na(IMR)) %>%
arrange(as.ordered(IMR)) %>%
mutate(Provinsi = factor(Provinsi, Provinsi)) %>%
ggplot(aes(x = Provinsi, y = IMR)) +
labs (title = "Angka Kematian Bayi" , x = " ", y = " ", subtitle = " Angka Kematian Bayi Per 1000 Kelahiran ",
caption = "Source : Central Bureau Of Statistic (BPS) | Plot generated by : Antonito") +
geom_bar(stat = "identity", fill = "brown") +
coord_flip() +
theme_ipsum()
p

ggplotly(p)
coba %>%
filter(!is.na(IMR)) %>%
arrange(IMR) %>%
mutate(Provinsi=factor(Provinsi, Provinsi)) %>%
ggplot(aes(x = Provinsi, y = IMR)) +
labs(title = "Angka Kematian Bayi" , x = " ", y = "Kematian Per 1.000 Kelahiran Hidup ", subtitle = "Provinsi dengan Angka Kematian Bayi Tertinggi",
caption = "Source : Central Bureau of Statistic (BPS), 2016 | Plot generated by : Antonito") +
geom_bar(stat = "identity", fill = "brown") +
coord_flip() +
theme_ipsum() +
theme(panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position = "none")

tmpp <- coba %>%
filter(!is.na(IMR)) %>%
arrange(desc(IMR)) %>%
mutate(Provinsi = factor(Provinsi, Provinsi))
tmpp
## Provinsi IMR
## 1 Sulawesi Barat 50.02
## 2 Papua 45.74
## 3 Papua Barat 44.95
## 4 Maluku 44.65
## 5 Nusa Tenggara Barat 43.30
## 6 Nusa Tenggara Timur 40.48
## 7 Gorontalo 36.71
## 8 Maluku Utara 35.68
## 9 Kalimantan Tengah 34.84
## 10 Sulawesi Tengah 34.76
## 11 Kalimantan Selatan 33.72
## 12 Sumatera Utara 31.54
## 13 Sumatera Barat 30.40
## 14 Bengkulu 30.10
## 15 Sumatera Selatan 28.48
## 16 Banten 27.85
## 17 Aceh 27.18
## 18 Kep. Riau 26.81
## 19 Sulawesi Selatan 26.46
## 20 Kalimantan Barat 25.85
## 21 Lampung 25.66
## 22 Indonesia 25.50
## 23 Kep. Bangka Belitung 25.45
## 24 Sulawesi Tenggara 24.77
## 25 Jambi 23.77
## 26 Jawa Timur 23.58
## 27 Sulawesi Utara 22.47
## 28 Riau 22.40
## 29 Jawa Tengah 22.07
## 30 Bali 20.93
## 31 Jawa Barat 18.08
## 32 Dki Jakarta 17.76
## 33 Kalimantan Timur 14.71
## 34 Di Yogyakarta 12.52
kematian = read.csv("bayi.csv", sep = ";")
p2 <- kematian %>%
filter(!is.na(AKB)) %>%
arrange(as.ordered(AKB)) %>%
mutate(Wilayah = factor(Wilayah, Wilayah)) %>%
ggplot(aes(x = Wilayah, y = AKB)) +
labs (title = "Angka Kematian Bayi Jawa Timur" , x = " ", y = "Kematian Per 1.000 Kelahiran Hidup ", subtitle = " Angka Kematian Bayi Per 1000 Kelahiran ",
caption = "Source : Central Bureau Of Statistic East Java (BPS) | Plot generated by : Antonito") +
geom_bar(stat = "identity", fill = "tomato4") +
coord_flip() +
theme_ipsum()
p2

ggplotly(p2)
k = kematian %>%
filter(!is.na(AKB)) %>%
arrange(AKB) %>%
mutate(Wilayah=factor(Wilayah, Wilayah)) %>%
ggplot(aes(x = Wilayah, y = AKB)) +
labs(title = "Angka Kematian Bayi" , x = " ", y = "Kematian Per 1.000 Kelahiran Hidup ", subtitle = "Kabupaten/Kota dengan Angka Kematian Bayi Tertinggi di Jatim",
caption = "Source : Central Bureau of Statistic (BPS) East Java, 2016 | Plot generated by : Antonito") +
geom_bar(stat = "identity", fill = "steelblue4") +
coord_flip() +
theme_ipsum() +
theme(panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position = "none")
k

ggplotly(k)